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Blind optimization of algorithm parameters for signal denoising by Monte-Carlo sure

Related publications (57)

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Robust speech recognition based on multi-stream processing

Astrid Hagen

Despite sophisticated present day automatic speech recognition (ASR) techniques, a single recognizer is usually incapable of accounting for the varying conditions in a typical natural environment. Higher robustness to a range of noise cases can potentially ...

EPFL2002

Wavelet de-noising for highly noisy source separation

Kamiar Aminian, Philippe Robert, Anisoara Ionescu, Bijan Najafi

The aim of this paper is to demonstrate that wavelet denoising processing is extremely attractive for efficient source separation of strong noisy mixtures. Systematic numerical simulations using source separation algorithms after wavelet de-noising are use ...

2002

Source separation in strong noisy mixtures: A study of wavelet de-noising pre-processing

Kamiar Aminian, Philippe Robert, Anisoara Ionescu, Bijan Najafi

This paper addresses the source separation in strong noisy mixtures by wavelet de-noising processing. Experiments include the cases of white/correlated Gaussian and non- Gaussian noise, which correspond to various applications. The performance of BSS/ICA a ...

2002

Robust speech recognition based on multi-stream processing

Astrid Hagen

École Polytechnique Fédérale de Lausanne2001

Robust speech recognition based on multi-stream processing

Astrid Hagen

2001

Properties of Smoothing with Time Gating

Marten Sjöström

Estimates of transfer functions from measurements contain undesired information -- noise -- whose effect can partly be taken away by applying different smoothing techniques. One such smoothing technique suggests to remove the noise by gating the impulse re ...

2000

Lossy compression and wavelet thresholding for image denoising

Martin Vetterli

In recent work, it was proposed to use lossy compression to remove noise from corrupted signals, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. To further understand a ...

1998